背景:AppleWatch(AW)系列1为轮椅使用者提供了能量消耗(EE),但被发现不准确,误差约为30%,FitbitCharge2提供的心率(HR)的相应误差约为10%至20%。使用这些智能手表(SWs)的较新版本,预计会提高估计的EE和HR的准确性。
目的:本研究旨在评估AW系列4(轮椅专用设置)和FitbitVersa(跑步机运行模式)在不同强度轮椅推进过程中估算EE和HR的准确性。
方法:数据来自20名手动轮椅使用者(男性:n=11,女性:n=9;体重:平均75,SD19kg)和20名非残疾人士(男性:n=11,女性:n=9;体重:平均75,SD11kg)。在3个单独的测试日(0.5%,2.5%,或5%倾斜),而EE和HR是通过标准设备和AW或Fitbit收集的。平均绝对百分比误差(MAPE)用于指示EE和HR的标准设备与SWs之间的绝对一致性。此外,线性混合模型分析评估了运动强度的影响,性别,和组的SW错误。类别间相关系数用于评估标准设备和SWs之间的相对一致性。
结果:AW低估了EE,轮椅使用者的MAPE为29.2%(SD22%),无残疾人士的MAPE为30%(SD12%)。Fitbit高估了EE,轮椅使用者的MAPE为73.9%(SD7%),无残疾人士的MAPE为44.7%(SD38%)。两个SWs都低估了HR。EE和HR的设备误差随两种SWs的强度而增加(所有比较:P<.001),两组之间唯一的显着差异是AW中的HR(轮椅使用者-5.27次/分钟;P=.02)。性别对EE的估计误差有显著影响,女性参与者的AW精度较差(-0.69kcal/min;P<.001),Fitbit精度较好(-2.08kcal/min;P<.001)。对于HR,仅在AW人群中发现性别差异,女性参与者的误差较小(5.23次/分钟;P=.02)。除2个阶段倾斜组合外,两个SWs的类别间相关系数均显示较差至中等的相对一致性(EE的AW:0.12-0.57,HR的AW:0.11-0.86;Fitbit:EE的0.06-0.85,HR的0.03-0.29)。
结论:AW和Fitbit均不足以估计轮椅推进期间的EE或HR。AW低估了EE,Fitbit高估了EE,两个SWs都低估了HR。因此,当使用SWs作为训练轮椅使用者的强度调节和能量平衡或不平衡的工具时,需要谨慎。
BACKGROUND: The Apple Watch (AW) Series 1 provides energy expenditure (EE) for wheelchair users but was found to be inaccurate with an error of approximately 30%, and the corresponding error for heart rate (HR) provided by the Fitbit Charge 2 was approximately 10% to 20%. Improved accuracy of estimated EE and HR is expected with newer editions of these smart watches (SWs).
OBJECTIVE: This study aims to assess the accuracy of the AW Series 4 (wheelchair-specific setting) and the Fitbit Versa (treadmill running mode) for estimating EE and HR during wheelchair propulsion at different intensities.
METHODS: Data from 20 manual wheelchair users (male: n=11, female: n=9; body mass: mean 75, SD 19 kg) and 20 people without a disability (male: n=11, female: n=9; body mass: mean 75, SD 11 kg) were included. Three 4-minute wheelchair propulsion stages at increasing speed were performed on 3 separate test days (0.5%, 2.5%, or 5% incline), while EE and HR were collected by criterion devices and the AW or Fitbit. The mean absolute percentage error (MAPE) was used to indicate the absolute agreement between the criterion device and SWs for EE and HR. Additionally, linear mixed model analyses assessed the effect of exercise intensity, sex, and group on the SW error. Interclass correlation coefficients were used to assess relative agreement between criterion devices and SWs.
RESULTS: The AW underestimated EE with MAPEs of 29.2% (SD 22%) in wheelchair users and 30% (SD 12%) in people without a disability. The Fitbit overestimated EE with MAPEs of 73.9% (SD 7%) in wheelchair users and 44.7% (SD 38%) in people without a disability. Both SWs underestimated HR. The device error for EE and HR increased with intensity for both SWs (all comparisons: P<.001), and the only significant difference between groups was found for HR in the AW (-5.27 beats/min for wheelchair users; P=.02). There was a significant effect of sex on the estimation error in EE, with worse accuracy for the AW (-0.69 kcal/min; P<.001) and better accuracy for the Fitbit (-2.08 kcal/min; P<.001) in female participants. For HR, sex differences were found only for the AW, with a smaller error in female participants (5.23 beats/min; P=.02). Interclass correlation coefficients showed poor to moderate relative agreement for both SWs apart from 2 stage-incline combinations (AW: 0.12-0.57 for EE and 0.11-0.86 for HR; Fitbit: 0.06-0.85 for EE and 0.03-0.29 for HR).
CONCLUSIONS: Neither the AW nor Fitbit were sufficiently accurate for estimating EE or HR during wheelchair propulsion. The AW underestimated EE and the Fitbit overestimated EE, and both SWs underestimated HR. Caution is hence required when using SWs as a tool for training intensity regulation and energy balance or imbalance in wheelchair users.